Environment controller and method for inferring via a neural network one or more commands for controlling an appliance

ABSTRACT

Method and environment controller for inferring via a neural network one or more commands for controlling an appliance. A predictive model generated by a neural network training engine is stored by the environment controller. The environment controller receives at least one environmental characteristic value (for example, at least one of a current temperature, current humidity level, current carbon dioxide level, and current room occupancy). The environment controller receives at least one set point (for example, at least one of a target temperature, target humidity level, and target carbon dioxide level). The environment controller executes a neural network inference engine, which uses the predictive model for inferring the one or more commands for controlling the appliance based on the at least one environmental characteristic value and the at least one set point. The environment controller transmits the one or more commands to the controlled appliance.

TECHNICAL FIELD

The present disclosure relates to the field of environment controlsystems. More specifically, the present disclosure relates to anenvironment controller and a method for inferring via a neural networkone or more commands for controlling an appliance.

BACKGROUND

Systems for controlling environmental conditions, for example inbuildings, are becoming increasingly sophisticated. A control system mayat once control heating and cooling, monitor air quality, detecthazardous conditions such as fire, carbon monoxide release, intrusion,and the like. Such control systems generally include at least oneenvironment controller, which receives measured environmentalcharacteristic values, generally from external sensors, and in turndetermines set points or command parameters to be sent to controlledappliances.

For instance, a room has current environmental characteristic values,such as a current temperature and a current humidity level, detected bysensors and reported to an environment controller. A user interacts withthe environment controller to provide set point(s), such as a targettemperature and/or a target humidity level. The environment controllersends the set point(s) to a controlled appliance (e.g. a heating,ventilating, and/or air-conditioning (HVAC) appliance). The controlledappliance generates commands for actuating internal components (of thecontrolled appliance) to reach the set point(s). Alternatively, theenvironment controller directly determines command(s) based on the setpoint(s), and transmits the command(s) to the controlled appliance. Thecontrolled appliance uses the command(s) received from the environmentcontroller to actuate the internal components to reach the set point(s).Examples of internal components include a motor, an electrical circuit(e.g. for generating heat), a valve (e.g. for controlling an air flow),etc.

However, the generation of the command(s) for actuating internalcomponents of the controlled appliance does not take into considerationthe current environmental characteristic values and the set point(s) incombination, to generate the most adequate command(s). The adequacy ofthe command(s) depends on one or more criteria, which can be taken intoconsideration individually or in combination. Examples of such criteriainclude a comfort of the people present in the room, stress imposed oncomponents of the controlled appliance (e.g. mechanical stress, heatingstress, etc.), energy consumption, etc.

For instance, we take the example where the current environmentalcharacteristic values include the current temperature of the room andthe set points include the target temperature of the room. In the caseof a significant difference between the current temperature and thetarget temperature (e.g. more than 5 degrees Celsius), the comfort ofthe people in the room shall be of prime importance. Thus, the generatedcommand(s) shall provide for a quick convergence from the currenttemperature to the target temperature. However, this quick convergencemay induce an increase of the stress imposed on components of thecontrolled appliance and a significant energy consumption. By contrast,in the case of a small difference between the current temperature andthe target temperature (e.g. less than 5 degrees Celsius), the comfortof the people in the room is not affected in a significant manner by thespeed at which the convergence from the current temperature to thetarget temperature is achieved. Therefore, the generated command(s)shall aim at preserving the controlled appliance (by minimizing thestress imposed on components of the controlled appliance) and minimizingenergy consumption.

A set of rules taking into consideration the current environmentalcharacteristic values and the set point(s) may be implemented by theenvironment controller, for generating the most adequate command(s).However, the criteria for evaluating the adequacy of the command(s)based on the current environmental characteristic values and the setpoint(s) are multiple, potentially complex, and generally inter-related.Thus, the aforementioned set of rules would either be too simple togenerate an effective model for generating the most adequate command(s),or alternatively too complicated to be designed by a human being.

However, current advances in artificial intelligence, and morespecifically in neural networks, can be taken advantage of. Morespecifically, a model, taking into consideration the currentenvironmental characteristic values and the set point(s) to generate themost adequate command(s) for controlling the appliance, can be generatedand used by a neural network.

Therefore, there is a need for a new environment controller and methodfor inferring via a neural network one or more commands for controllingan appliance.

SUMMARY

According to a first aspect, the present disclosure relates to anenvironment controller. The environment controller comprises acommunication interface, memory for storing a predictive model generatedby a neural network training engine, and a processing unit. Theprocessing unit receives at least one environmental characteristic valuevia the communication interface. The processing unit receives at leastone set point via at least one of the communication interface and a userinterface of the environment controller. The processing unit executes aneural network inference engine. The neural network inference engineuses the predictive model for inferring one or more commands forcontrolling an appliance based on the at least one environmentalcharacteristic value and the at least one set point. The processing unittransmits the one or more commands to the controlled appliance via thecommunication interface.

According to a second aspect, the present disclosure relates to a methodfor inferring via a neural network one or more commands for controllingan appliance. The method comprises storing a predictive model generatedby a neural network training engine in a memory of an environmentcontroller. The method comprises receiving, by a processing unit of theenvironment controller, at least one environmental characteristic valuevia a communication interface of the environment controller. The methodcomprises receiving, by the processing unit, at least one set point viaat least one of the communication interface and a user interface of theenvironment controller. The method comprises executing, by theprocessing unit, a neural network inference engine. The neural networkinference engine uses the predictive model for inferring the one or morecommands for controlling the appliance based on the at least oneenvironmental characteristic value and the at least one set point. Themethod comprises transmitting, by the processing unit, the one or morecommands to the controlled appliance via the communication interface.

According to a third aspect, the present disclosure relates to anon-transitory computer program product comprising instructionsexecutable by a processing unit of an environment controller. Theexecution of the instructions by the processing unit of the environmentcontroller provides for inferring via a neural network one or morecommands for controlling an appliance. More specifically, the executionof the instructions provides for storing a predictive model generated bya neural network training engine in a memory of the environmentcontroller. The execution of the instructions provides for receiving bythe processing unit at least one environmental characteristic value viaa communication interface of the environment controller. The executionof the instructions provides for receiving, by the processing unit, atleast one set point via at least one of the communication interface anda user interface of the environment controller. The execution of theinstructions provides for executing, by the processing unit, a neuralnetwork inference engine. The neural network inference engine uses thepredictive model for inferring the one or more commands for controllingthe appliance based on the at least one environmental characteristicvalue and the at least one set point. The execution of the instructionsprovides for transmitting, by the processing unit, the one or morecommands to the controlled appliance via the communication interface.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the disclosure will be described by way of example onlywith reference to the accompanying drawings, in which:

FIG. 1 illustrates an environment controller capable of inferring via aneural network one or more commands for controlling an appliance;

FIG. 2 illustrates an exemplary environment control system where theenvironment controller of FIG. 1 is deployed;

FIG. 3 illustrates a method implemented by the environment controller ofFIGS. 1 and 2 for inferring via a neural network one or more commandsfor controlling the appliance of FIGS. 1 and 2;

FIG. 4 represents an environment control system where environmentcontrollers implementing the method illustrated in FIG. 3 are deployed;and

FIG. 5 is a schematic representation of a neural network inferenceengine executed by the environment controller of FIGS. 1 and 2.

DETAILED DESCRIPTION

The foregoing and other features will become more apparent upon readingof the following non-restrictive description of illustrative embodimentsthereof, given by way of example only with reference to the accompanyingdrawings.

Various aspects of the present disclosure generally address one or moreof the problems related to an optimization of command(s) sent by anenvironment controller to a controlled appliance based on currentenvironmental conditions and target environmental conditions (alsoreferred to as set points in the present disclosure).

TERMINOLOGY

The following terminology is used throughout the present disclosure:

Environment: condition(s) (temperature, humidity, pressure, oxygenlevel, carbon dioxide level, light level, security, etc.) prevailing ina controlled area or place, such as for example in a building.

Environment control system: a set of components which collaborate formonitoring and controlling an environment.

Environmental data: any data (e.g. information, commands) related to anenvironment that may be exchanged between components of an environmentcontrol system.

Environment control device (ECD): generic name for a component of anenvironment control system. An ECD may consist of an environmentcontroller, a sensor, a controlled appliance, etc.

Environment controller: device capable of receiving information relatedto an environment and sending commands based on such information.

Environmental characteristic: measurable, quantifiable or verifiableproperty of an environment.

Environmental characteristic value: numerical, qualitative or verifiablerepresentation of an environmental characteristic.

Sensor: device that detects an environmental characteristic and providesa numerical, quantitative or verifiable representation thereof. Thenumerical, quantitative or verifiable representation may be sent to anenvironment controller.

Controlled appliance: device that receives a command and executes thecommand. The command may be received from an environment controller.

Environmental state: a current condition of an environment based on anenvironmental characteristic, each environmental state may comprise arange of values or verifiable representation for the correspondingenvironmental characteristic.

VAV appliance: A Variable Air Volume appliance is a type of heating,ventilating, and/or air-conditioning (HVAC) system. By contrast to aConstant Air Volume (CAV) appliance, which supplies a constant airflowat a variable temperature, a VAV appliance varies the airflow at aconstant temperature.

Referring now concurrently to FIGS. 1, 2 and 3, an environmentcontroller 100 (represented in FIGS. 1 and 2) and a method 500(represented in FIG. 3) for inferring via a neural network one or morecommands for controlling an appliance are illustrated.

The environment controller 100 comprises a processing unit 110, memory120, a communication interface 130, optionally a user interface 140, andoptionally a display 150. The environment controller 100 may compriseadditional components not represented in FIG. 1 for simplificationpurposes.

The processing unit 110 comprises one or more processors (notrepresented in FIG. 1) capable of executing instructions of a computerprogram. Each processor may further comprise one or several cores.

The memory 120 stores instructions of computer program(s) executed bythe processing unit 110, data generated by the execution of the computerprogram(s), data received via the communication interface 130, datareceived via the optional user interface 140, etc. Only a single memory120 is represented in FIG. 1, but the environment controller 100 maycomprise several types of memories, including volatile memory (such as avolatile Random Access Memory (RAM)) and non-volatile memory (such as ahard drive).

The communication interface 130 allows the environment controller 100 toexchange data with several devices (e.g. a training server 200, one ormore sensors 300, one or more controlled appliances 400, etc.) over oneor more communication network (not represented in FIG. 1 forsimplification purposes). The term communication interface 130 shall beinterpreted broadly, as supporting a single communicationstandard/technology, or a plurality of communicationstandards/technologies. Examples of communication interfaces 130 includea wireless (e.g. Wi-Fi, cellular, wireless mesh, etc.) communicationmodule, a wired (e.g. Ethernet) communication module, a combination ofwireless and wired communication modules, etc. In an exemplaryconfiguration, the communication interface 130 of the environmentcontroller 100 has a first wireless (e.g. Wi-Fi) communication modulefor exchanging data with the sensor(s) and the controlled appliance(s),and a second wired (e.g. Ethernet) communication module for exchangingdata with the training server 200.

At least some of the steps of the method 500 are implemented by theenvironment controller 100, to infer via a neural network one or morecommands for controlling the controlled appliance 400.

A dedicated computer program has instructions for implementing at leastsome of the steps of the method 500. The instructions are comprised in anon-transitory computer program product (e.g. the memory 120) of theenvironment controller 100. The instructions provide for inferring via aneural network one or more commands for controlling the controlledappliance 400, when executed by the processing unit 110 of theenvironment controller 100. The instructions are deliverable to theenvironment controller 100 via an electronically-readable media such asa storage media (e.g. CD-ROM, USB key, etc.), or via communication links(e.g. via a communication network through the communication interface130).

The dedicated computer program executed by the processing unit 110comprises a neural network inference engine 112 and a control module114.

Also represented in FIG. 1 is the training server 200. Although notrepresented in FIG. 1 for simplification purposes, the training server200 comprises a processing unit, memory and a communication interface.The processing unit of the training server 200 executes a neural networktraining engine 211.

The execution of the neural network training engine 211 generates apredictive model, which is transmitted to the environment controller 100via the communication interface of the training server 200. For example,the predictive model is transmitted over a communication network andreceived via the communication interface 130 of the environmentcontroller 100.

Also represented in FIG. 1 are the sensors 300. Although not representedin FIG. 1 for simplification purposes, the sensors 300 comprise at leastone sensing module for detecting an environmental characteristic, and acommunication interface for transmitting to the environment controller100 an environmental characteristic value corresponding to the detectedenvironmental characteristic. The environmental characteristic value istransmitted over a communication network and received via thecommunication interface 130 of the environment controller 100.

FIG. 2 illustrates examples of sensors 300 and corresponding examples oftransmitted environmental characteristic value(s). The examples includea temperature sensor 310, capable of measuring a current temperature andtransmitting the measured current temperature to the environmentcontroller 100. The examples also include a humidity sensor 320, capableof measuring a current humidity level and transmitting the measuredcurrent humidity level to the environment controller 100. The examplesfurther include a carbon dioxide (CO2) sensor 330, capable of measuringa current CO2 level and transmitting the measured current CO2 level tothe environment controller 100. The examples also include a roomoccupancy sensor 340, capable of determining a current occupancy of aroom and transmitting the determined current room occupancy to theenvironment controller 100. The room comprises the sensors 300 and thecontrolled appliance 400. However, the environment controller 100 may ormay not be present in the room (the environment controller 100 mayremotely control the environment of the room, which includes controllingthe controlled appliance 400 based on the inputs of the sensors 300).

The aforementioned examples of sensors 300 are for illustration purposesonly, and a person skilled in the art would readily understand thatother types of sensors 300 could be used in the context of anenvironment control system managed by the environment controller 100.Furthermore, each environmental characteristic value may consist ofeither a single value (e.g. current temperature of 25 degrees Celsius),or a range of values (e.g. current temperature from 25 to 26 degreesCelsius).

The temperature, humidity and CO2 sensors are well known in the art, andeasy to implement types of sensors. With respect to the occupancysensor, its implementation may be more or less complex, based on itscapabilities. For example, a basic occupancy sensor (e.g. based onultrasonic or infrared technology) is only capable of determining if aroom is occupied or not. A more sophisticated occupancy sensor iscapable of determining the number of persons present in a room, and mayuse a combination of camera(s) and pattern recognition software for thispurpose. Consequently, in the context of the present disclosure, asensor 300 shall be interpreted as potentially including several devicescooperating for determining an environmental characteristic value (e.g.one or more cameras collaborating with a pattern recognition softwareexecuted by a processing unit for determining the current number ofpersons present in the room).

Also represented in FIG. 1 is the controlled appliance 400. Although notrepresented in FIG. 1 for simplification purposes, the controlledappliance 400 comprises at least one actuation module, and acommunication interface for receiving one or more commands from theenvironment controller 100. The actuation module can be of one of thefollowing type: mechanical, pneumatic, hydraulic, electrical,electronical, a combination thereof, etc. The one or more commandscontrol operations of the at least one actuation module. The one or morecommands are transmitted over a communication network via thecommunication interface 130 of the environment controller 100.

FIG. 2 illustrates an example of a controlled appliance 410, consistingof a VAV appliance. Examples of commands transmitted to the VAVappliance 410 include commands directed to one of the following: anactuation module controlling the speed of a fan, an actuation modulecontrolling the pressure generated by a compressor, an actuation modulecontrolling a valve defining the rate of an airflow, etc. This exampleis for illustration purposes only, and a person skilled in the art wouldreadily understand that other types of controlled appliances 400 couldbe used in the context of an environment control system managed by theenvironment controller 100.

Also represented in FIG. 1 is a user 10. The user 10 provides at leastone set point to the environment controller 100. Examples of set pointsinclude target environmental characteristic values, such as a targettemperature, a target humidity level, a target CO2 level, a combinationthereof, etc. The at least one set point is related to the room wherethe sensors 300 and the controlled appliance 400 are located.Alternatively, the controlled appliance 400 is not located in the room,but the operations of the controlled appliance 400 under the supervisionof the environment controller 100 aim at reaching the at least one setpoint in the room. The user enters the at least one set point via theuser interface 140 of the environment controller 100. Alternatively, theuser enters the at least one set point via a user interface of acomputing device (e.g. a smartphone, a tablet, etc.) not represented inFIG. 1 for simplification purposes; and the at least one set point istransmitted over a communication network and received via thecommunication interface 130 of the environment controller 100.

FIG. 2 illustrates examples of set points, comprising a targettemperature, a target humidity level and a target CO2 level. Theseexamples are for illustration purposes only, and a person skilled in theart would readily understand that other types of set points could beused in the context of an environment control system managed by theenvironment controller 100. Furthermore, each set point may consist ofeither a single value (e.g. target temperature of 25 degrees Celsius),or a range of values (e.g. target temperature from 25 to 26 degreesCelsius).

The method 500 comprises the step 505 of executing the neural networktraining engine 211 (by the processing unit of the training server 200)to generate the predictive model.

The method 500 comprises the step 510 of transmitting the predictivemodel to the environment controller 100, via the communication interfaceof the training server 200.

The method 500 comprises the step 515 of storing the predictive model inthe memory 120 of the environment controller 100. The predictive modelis received via the communication interface 130 of the environmentcontroller 100, and stored in the memory 120 by the processing unit 110.

The method 500 comprises the step 520 of receiving the at least oneenvironmental characteristic value from the at least one sensor 300. Theat least one environmental characteristic value is received by theprocessing unit 110 via the communication interface 130. Step 520 isperformed by the control module 114 executed by the processing unit 110.

The method 500 comprises the step 525 of receiving the at least one setpoint from the user 10. The at least one set point is received by theprocessing unit 110 via the user interface 140 and/or the communicationinterface 130. Step 525 is performed by the control module 114 executedby the processing unit 110.

The method 500 comprises the step 530 of executing the neural networkinference engine 112 (by the processing unit 110). The neural networkinference engine 112 uses the predictive model (stored in memory 120 atstep 515) for inferring one or more commands for controlling theappliance 400, based on the at least one environmental characteristicvalue (received at step 520) and the at least one set point (received atstep 525).

The method 500 comprises the step 535 of transmitting the one or morecommands to the controlled appliance 400 via the communication interface130.

The method 500 comprises the step 540 of applying by the controlledappliance 400 the one or more commands received from the environmentcontroller 100.

Steps 530, 535 and 540 are repeated if new parameters are received atsteps 520 (one or more new environmental characteristic value) and/or525 (one or more new set point). Furthermore, configurable thresholdscan be used for the parameters received at steps 520 and 525, so that achange in the value of a parameter is not taken into consideration aslong as it remains within the boundaries of the correspondingthreshold(s). For example, if the parameter is a new current temperaturereceived at step 520, the threshold can be an increment/decrease of 1degree Celsius in the current temperature. If the parameter is a newtarget temperature received at step 525, the threshold can be anincrement/decrease of 0.5 degree Celsius in the target temperature.Additionally, the control module 114 may discard any new environmentcharacteristic value received at step 520 unless a new set point isreceived at step 525.

Reference is now made to FIG. 1, and more particularly to the neuralnetwork inference engine 112 and the neural network training engine 211.

Various criteria may be taken into consideration for optimizing the oneor more commands generated by the environment controller 100 forcontrolling the appliance 400, based on at least one environmentalcharacteristic value and at least one set point. As previouslymentioned, such criteria include a comfort of the people present in theroom where the controlled appliance 400 is located, stress imposed oncomponents of the controlled appliance 400 (e.g. mechanical stress,heating stress, etc.), energy consumption of the controlled appliance400, etc. These criteria are for illustration purposes only, and are notlimitative.

The present disclosure aims at providing a mechanism for inferring anoptimal set of command(s) for controlling the appliance 400 based on theaforementioned criteria, whatever the at least one environmentalcharacteristic value and the at least one set point may be. Themechanism disclosed in the present disclosure takes advantage of theneural network technology, to “guess” the optimal set of command(s) inoperating conditions, based on a model generated during a trainingphase.

A first type of data used as inputs of the neural network trainingengine 211 (during a training phase) and the neural network inferenceengine 112 (during an operational phase) consists of one or moreenvironmental characteristic value(s). Examples of environmentalcharacteristic values have already been described, but the presentdisclosure is not limited by those examples. A person skilled in the artof environmental control systems would readily adapt the type ofenvironmental characteristic value(s) to a specific type of controlledappliance 400.

A second type of data used as inputs of the neural network trainingengine 211 (during a training phase) and the neural network inferenceengine 112 (during an operational phase) consists of one or more setpoint(s). Examples of set points have already been described, but thepresent disclosure is not limited by those examples. A person skilled inthe art of environmental control systems would readily adapt the type ofset point(s) to a specific type of controlled appliance 400.

The output(s) of the neural network training engine 211 (during atraining phase) and the neural network inference engine 112 (during anoperational phase) consists of one or more commands for controlling theappliance 400. Examples of commands have already been described, but thepresent disclosure is not limited by those examples. A person skilled inthe art of environmental control systems would readily adapt the type ofcommands to a specific type of controlled appliance 400.

Combinations of at least one environmental characteristic value, atleast one set point, and one or more command(s) for the controlledappliance 400, are taken into consideration by the neural networkinference engine 112 and the neural network training engine 211. Thebest combinations can be determined during the training phase with theneural network training engine 211. The best combinations may depend onthe type of controlled appliance 400, on criteria for evaluating theadequacy of the one or more command(s), on characteristics of the roomwhere the controlled appliance 400 is located, etc.

Examples of criteria for evaluating the adequacy of the one or morecommand(s) have already been described, but the present disclosure isnot limited by those examples. A person skilled in the art ofenvironmental control systems would readily adapt the criteria to aspecific type of controlled appliance 400.

The training phase is used to identify the best combinations of inputand output parameters, and only those parameters will be used by theneural network training engine 211 to generate the predictive model usedby the neural network inference engine 112. Alternatively, all theavailable parameters can be used by the neural network training engine211 to generate the predictive model. In this case, the neural networktraining engine 211 will simply learn to ignore some of the inputparameters which do not have a significant influence on the one or morecommands for controlling the appliance 400.

During the training phase, the neural network training engine 211 istrained with a plurality of inputs (each input comprises at least oneenvironmental characteristic value and at least one set point) and acorresponding plurality of outputs (each corresponding output comprisesone or more commands for the controlled appliance 400). As is well knownin the art of neural network, during the training phase, the neuralnetwork implemented by the neural network training engine 211 adjustsits weights. Furthermore, during the training phase, the number oflayers of the neural network and the number of nodes per layer can beadjusted to improve the accuracy of the model. At the end of thetraining phase, the predictive model generated by the neural networktraining engine 211 includes the number of layers, the number of nodesper layer, and the weights.

The inputs and outputs for the training phase of the neural network canbe collected through an experimental process. For example, a pluralityof combinations of current temperature and target temperature aretested. For each combination, a plurality of sets of command(s) aretested, and the most adequate set of command(s) is determined based oncriteria for evaluating the adequacy. For example, the criteria arecomfort for the user and energy consumption. In a first case, thecurrent temperature is 30 degrees Celsius and the target temperature is22 degrees Celsius. For simplification, purposes, the set of commandsonly consists in setting the operating speed of a fan of the controlledappliance 400. The following speeds are available: 5, 10, 15, 20 and 25revolutions per second. For obtaining a quick transition from 30 to 22degrees Celsius (to maximize the comfort of the persons present in theroom) while preserving energy, it is determined experimentally (ortheoretically) that the best speed is 20 revolutions per second. In asecond case, the current temperature is 24 degrees Celsius and thetarget temperature is 22 degrees Celsius. For obtaining a transitionfrom 24 to 22 degrees Celsius (the comfort of the persons present in theroom is not substantially affected in this case) while preservingenergy, it is determined experimentally (or theoretically) that the bestspeed is 5 revolutions per second (to maximize the energy savings).Thus, the neural network training engine 211 is fed with the followingcombinations of data: [current temperature 30, target temperature 22,fan speed 20] and [current temperature 24, target temperature 22, fanspeed 5].

As mentioned previously in the description, the environmentalcharacteristic values (e.g. current temperature) and set points (e.g.target temperature) can be expressed either as a single value or as arange of values.

Although a single command has been taken into consideration forsimplification purposes, several commands may be considered incombination. For example, the speed of the fan may be evaluated incombination with the pressure generated by a compressor of thecontrolled appliance 400 for evaluating the most adequate set ofcommands for transitioning from 30 to 22 degrees Celsius and from 24 to22 degrees Celsius. Furthermore, more than one set point and/or morethan one current environmental value may be used. For example, inaddition to the target temperature, a target humidity level is fixed,and various sets of command(s) are evaluated based on the criteria (usercomfort, energy consumption, etc.) for performing the transition fromthe current temperature/humidity level to the targettemperature/humidity level. Then, still other current environmentalvalue(s) may be taken into consideration (e.g. room occupancy and/or CO2level) for performing the transition from the currenttemperature/humidity level to the target temperature/humidity level.

Various techniques well known in the art of neural networks are used forperforming (and improving) the generation of the predictive model, suchas forward and backward propagation, usage of bias in addition to theweights (bias and weights are generally collectively referred to asweights in the neural network terminology), reinforcement training, etc.

During the operational phase, the neural network inference engine 112uses the predictive model (e.g. the values of the weights) determinedduring the training phase to infer an output (one or more commands forcontrolling the appliance 400) based on inputs (at least oneenvironmental characteristic value received from the sensor(s) 300 andat least one set point received from the user 10), as is well known inthe art.

Reference is now made concurrently to FIGS. 1, 2, 3 and 4, where FIG. 4illustrates the usage of the method 500 in a large environment controlsystem.

A first plurality of environment controllers 100 implementing the method500 are deployed at a first location. Only two environment controllers100 are represented for illustration purposes, but any number ofenvironment controllers 100 may be deployed.

A second plurality of environment controllers 100 implementing themethod 500 are deployed at a second location. Only one environmentcontroller 100 is represented for illustration purposes, but any numberof environment controllers 100 may be deployed.

The first and second locations may consist of different buildings,different floors of the same building, etc. Only two locations arerepresented for illustration purposes, but any number of locations maybe considered.

Each environment controller 100 represented in FIG. 4 interacts with atleast one sensor 300, at least one user 10, and at least one controlledappliance 400, as illustrated in FIG. 1.

The environment controllers 100 correspond to the environmentcontrollers represented in FIG. 1, and execute both the control module114 and the neural network inference engine 112. Each environmentcontroller 100 receives a predictive model from the centralized trainingserver 200 (e.g. a cloud based training server 200 in communication withthe environment controllers 100 via a networking infrastructure, as iswell known in the art). The same predictive model is used for all theenvironment controllers. Alternatively, a plurality of predictive modelsis generated, and takes into account specific operating conditions ofthe environment controllers 100. For example, a first predictive modelis generated for the environment controllers 100 controlling a firsttype of appliance 400, and a second predictive model is generated forthe environment controllers 100 controlling a second type of appliance400.

FIG. 4 illustrates a decentralized architecture, where the environmentcontrollers 100 take autonomous decisions for controlling the appliances400, using the predictive model as illustrated in the method 500.

Reference is now made to FIG. 5, which illustrates the aforementionedneural network inference engine with its inputs and its output. FIG. 5corresponds to the neural network inference engine 112 executed at step530 of the method 500, as illustrated in FIGS. 1 and 3.

Although the present disclosure has been described hereinabove by way ofnon-restrictive, illustrative embodiments thereof, these embodiments maybe modified at will within the scope of the appended claims withoutdeparting from the spirit and nature of the present disclosure.

What is claimed is:
 1. An environment controller, comprising: acommunication interface; memory for storing a predictive model generatedby a neural network training engine, the predictive model comprisingweights of a neural network determined by the neural network trainingengine; and a processing unit comprising at least one processor for:receiving via the communication interface a current temperature, acurrent humidity level and a current room occupancy; receiving via oneof the communication interface and a user interface of the environmentcontroller a target temperature; executing a neural network inferenceengine, the neural network inference engine implementing a neuralnetwork using the predictive model for inferring an output based oninputs, the output comprising one or more command for controlling anappliance, the inputs comprising the current temperature, the currenthumidity level, the current room occupancy and the target temperature;and transmitting the one or more command to the controlled appliance viathe communication interface.
 2. The environment controller of claim 1,wherein the controlled appliance consists of a Variable Air Volume (VAV)appliance.
 3. The environment controller of claim 1, wherein the one ormore command for controlling the appliance include at least one of thefollowing: a command for controlling a speed of a fan, a command forcontrolling a pressure generated by a compressor, and a command forcontrolling a rate of an airflow through a valve.
 4. The environmentcontroller of claim 1, wherein the processing unit further receives viathe communication interface at least one additional environmentalcharacteristic value; and wherein the inputs of the neural networkinference engine further comprise the at least one additionalenvironmental characteristic value.
 5. The environment controller ofclaim 4, wherein the at least one additional environmentalcharacteristic value comprises a current carbon dioxide (CO2) level. 6.The environment controller of claim 1, wherein the processing unitfurther receives via one of the communication interface and the userinterface at least one additional set point; and wherein the inputs ofthe neural network inference engine further comprise the at least oneadditional set point.
 7. The environment control system of claim 6,wherein the at least one additional set point comprises at least one ofthe following: a target humidity level and a target CO2 level.
 8. Theenvironment controller of claim 1, wherein the processing unit furtherreceives via the communication interface a current carbon dioxide (CO2)level and further receives via one of the communication interface andthe user interface a target CO2 level; and wherein the inputs of theneural network inference engine further comprise the current CO2 leveland the target CO2 level.
 9. The environment control system of claim 1,wherein the current room occupancy consists of a determination whetherthe room is occupied or not, or consists of a number of persons presentin the room.
 10. A method for inferring via a neural network one or morecommand for controlling an appliance, the method comprising: storing apredictive model generated by a neural network training engine in amemory of an environment controller, the predictive model comprisingweights of a neural network determined by the neural network trainingengine; receiving by a processing unit of the environment controller viaa communication interface of the environment controller a currenttemperature, a current humidity level and a current room occupancy;receiving by the processing unit via one of the communication interfaceand a user interface of the environment controller a target temperature;executing by the processing unit a neural network inference engine, theneural network inference engine implementing a neural network using thepredictive model for inferring an output based on inputs, the outputcomprising the one or more command for controlling the appliance, theinputs comprising the current temperature, the current humidity level,the current room occupancy and the target temperature; and transmittingby the processing unit the one or more command to the controlledappliance via the communication interface.
 11. The method of claim 10,wherein the controlled appliance consists of a Variable Air Volume (VAV)appliance.
 12. The method of claim 10, wherein the one or more commandfor controlling the appliance include at least one of the following: acommand for controlling a speed of a fan, a command for controlling apressure generated by a compressor, and a command for controlling a rateof an airflow through a valve.
 13. The method of claim 10, furthercomprising receiving by the processing unit via the communicationinterface at least one additional environmental characteristic value;and wherein the inputs of the neural network inference engine furthercomprise the at least one additional environmental characteristic value.14. The method of claim 13, wherein the at least one additionalenvironmental characteristic value comprises a current carbon dioxide(CO2) level.
 15. The method of claim 10, further comprising receiving bythe processing unit via one of the communication interface and the userinterface at least one additional set point; and wherein the inputs ofthe neural network inference engine further comprise the at least oneadditional set point.
 16. The method of claim 15, wherein the at leastone additional set point comprises at least one of the following: atarget humidity level and a target CO2 level.
 17. The method of claim10, further comprising receiving by the processing via the communicationinterface a current carbon dioxide (CO2) level and further receiving viaone of the communication interface and the user interface a target CO2level; and wherein the inputs of the neural network inference enginefurther comprise the current CO2 level and the target CO2 level.
 18. Themethod of claim 10, wherein the current room occupancy consists of adetermination whether the room is occupied or not, or consists of anumber of persons present in the room.
 19. A non-transitory computerprogram product storing instructions executable by a processing unit ofan environment controller, the execution of the instructions by theprocessing unit of the environment controller providing for inferringvia a neural network one or more command for controlling an applianceby: storing a predictive model generated by a neural network trainingengine in a memory of the environment controller, the predictive modelcomprising weights of a neural network determined by the neural networktraining engine; receiving by the processing unit via a communicationinterface of the environment controller a current temperature, a currenthumidity level and a current room occupancy; receiving by the processingunit via one of the communication interface and a user interface of theenvironment controller a target temperature; executing by the processingunit a neural network inference engine, the neural network inferenceengine implementing a neural network using the predictive model forinferring an output based on inputs, the output comprising the one ormore command for controlling the appliance, the inputs comprising thecurrent temperature, the current humidity level, the current roomoccupancy and the target temperature; and transmitting by the processingunit the one or more command to the controlled appliance via thecommunication interface.